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SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIBUTION FOR STABILIZING COLORECTAL NBI ENDOSCOPY RECOGNITION

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ICIP 2013 | 2013 IEEE International Conference on Image Processing | September 15 - 18, 2013 | Melbourne, Australia

Veröffentlicht in: Technologie, Business
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SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIBUTION FOR STABILIZING COLORECTAL NBI ENDOSCOPY RECOGNITION

  1. 1. Smoothing Posterior Probabilities with a Particle Filter of Dirichlet Distribution for Stabilizing Colorectal NBI Endoscopy Recognition Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Yoko Kominami, Rie Miyaki, Taiji Matsuo, Shigeto Yoshida, Shinji Tanaka Hiroshima University, Japan Sep. 17. 2013
  2. 2. Colorectal cancer •  45,000 people have died from this cancer each year. •  The 3th leading cause of cancer death in Japan. Colorectal tumor must be found as early as possible! 1 0"" 20"" 40"" 60"" 80"" 100"" stage 1 stage 2 stage 3 stage 4 5 year survival rate of colorectal tumor Early stage End stage survivalrate[%] Time trend of the death by colorectal cancer 0" 10,000" 20,000" 30,000" 40,000" 50,000" '90" '91" '92" '93" '94" '95" '96" '97" '98" '99" '00" '01" '02" '03" '04" '05" '06" '07" '08" '09" fatalitiesofcolorectalcancer year •  5-year survival rate keeps in high percentage in early stage. •  Early finding of colorectal tumor causes complete cure.
  3. 3. Endoscopy examination with NBI 2 •  Narrow-Banded Imaging (NBI) system •  Enable us to enhance microvessel structure of polyps. Normal NBI Polyp Type A Type B Type C 1 2 3 Microvessels are not observed or extremely opaque. Fine microvessels are observed around pits, and clear pits can be observed via the nest of microvessels. Microvessels comprise an irregular network, pits observed via the microvessels are slightly non-distinct, and vessel diameter or distribution is homogeneous. Microvessels comprise an irregular network, pits observed via the microvessels are irregular, and vessel diameter or distribution is heterogeneous. Pits via the microvessels are invisible, irregular vessel diameter is thick, or the vessel distribution is heterogeneous, and a vascular areas are observed. Narrow-Band Imaging (NBI) magnification findings Normal Advanced Cancer
  4. 4. 4 Colorectal tumor classification in magnifying endoscopic NBI images [Tamaki et al., ACCV2010, MedIA2013] •  Feature: Bag-of-Visual-Words of densely sampled SIFT •  Classifier: Linear SVM •  Accuracy: 96% Real-time recognition system [Tamaki et al., MedIA2013] Extended to recognition of NBI video Display posterior probabilities at each frame.
  5. 5. Problem ~Real-time Recognition System~ 5 The output is highly unstable 0 0.5 1 251" 271" 291" 311" 331" 351" 371" 391" 411" 431" Probability Frame number A B C 0 20 40 60 80 120100 140 160 180 200 Estimated label Probability of type A Probability of type B Probability of type C3
  6. 6. Previous work 1 6 Smoothing of “curves” [Yokota et al., SSII2012] •  No probabilistic interpretation. •  Smoothing requires normalization to ensure that probabilities sum to 1. Problem 0 20 40 60 80 100 120 140 160 180 200 0 0.5 1 Time probability 0 0.5 1 0 20 40 60 80 100 120 140 160 180 200 Probability Type A Type B Type C3 Frame number •  Kalman Filter (x, ẋ and ẍ) Input Output
  7. 7. Previous work 2 7 Sequence Labeling [Hirakawa et al., EMBC2013] Type A Type B Type C3 Type B_1 (original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (DP_0.99) frame number 0 20 40 60 80 100 120 140 160 180 200 0 0.5 1 251" 271" 291" 311" 331" 351" 371" 391" 411" 431" Frame number A B C 0 20 40 60 80 120100 140 160 180 200Type B_1 (original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (DP_0.99) frame number 0 20 40 60 80 100 120 140 160 180 200 •  Map estimation of MRF •  Output is labels assigned to each frame Labels applied MAP estimation Output Input
  8. 8. Previous work 2 8 Sequence Labeling [Hirakawa et al., EMBC2013] Type A Type B Type C3 Type B_1 (original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (DP_0.99) frame number 0 20 40 60 80 100 120 140 160 180 200 0 0.5 1 251" 271" 291" 311" 331" 351" 371" 391" 411" 431" Frame number A B C 0 20 40 60 80 120100 140 160 180 200Type B_1 (original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (DP_0.99) frame number 0 20 40 60 80 100 120 140 160 180 200 •  Map estimation of MRF •  Output is labels assigned to each frame Labels applied MAP estimation Output Input •  Labels are LESS informative than probabilities. !  We have examined about how we should display the recognition results. Problem
  9. 9. Motivation 9 !  To support decisions by endoscopists during an endoscopy examination Visualize temporally smoothed and stabilized posterior probability curves. Objective •  Sequential online Bayes filtering •  Introducing the Dirichlet distribution as transition and likelihood •  Implemented with the Particle filtering. Probabilistic Approach
  10. 10. Sequential Filtering 10 xt = xt (A) , xt (B) , xt (C3) ( ), xt A( ) + xt B( ) + xt C3( ) =1State vector: Observation vector: yt = yt A( ) , yt B( ) , yt C3( ) ( ), yt A( ) + yt B( ) + yt C3( ) =1 We use Dirichlet distribution for state transition and likelihood. Prediction p xt y1:t−1( )= p xt xt−1( )∫ p xt−1 y1:t−1( )dxt Filtering p xt y1:t( )∝ p yt xt−1( ) p xt y1:t−1( ) State transition Likelihood Observation to t-1State of t Observation to tState of t ※ t : time
  11. 11. Dirichlet distribution 11 Dirλ1…K α1…K[ ]= Γ αkk=1 K ∑# $% & '( Γ αk[ ]k=1 K ∏ λk αk −1 k=1 K ∏ (0.50, 0.50, 0.50) (0.85, 1.50, 2.00) (1.00, 1.00, 1.00) (1.00, 1.76, 2.35) (4.00, 4.00 ,4.00) (3.40, 6.00, 8.00) low high α1…K : parameter of distribution
  12. 12. Sequential Filtering 12 xt = xt (A) , xt (B) , xt (C3) ( ), xt A( ) + xt B( ) + xt C3( ) =1State vector: Observation vector: yt = yt A( ) , yt B( ) , yt C3( ) ( ), yt A( ) + yt B( ) + yt C3( ) =1 We use Dirichlet distribution for state transition and likelihood. Prediction p xt y1:t−1( )= p xt xt−1( )∫ p xt−1 y1:t−1( )dxt Filtering p xt y1:t( )∝ p yt xt−1( ) p xt y1:t−1( ) State transition Likelihood
  13. 13. Proposed method ~state transition~ 13 p xt xt−1,θ1( )= Dirxt α1 θ1, xt−1( )"# $% •  We define the transition as Dirichlet. !  To enforce xt to be close to xt-1. !  With a single parameter θ1 to control the shape of the distribution. α1 θ1, xt−1( )=θ1xt−1 MAP estimate of xt-1 θ1=1 θ1=100 Should be distributed around xt-1 θ1=?
  14. 14. Proposed method ~likelihood~ 14 p yt xt,θ2( )= Dirxt α2 θ2, yt( )!" #$ α2 θ2, yt( )=θ2 yt + b •  We define the likelihood as Dirichlet. !  To enforce xt to be close to yt. !  With a single parameter θ2 and additional bias (+b) to control the shape of the distribution. The value of yt θ2=100, b=0 θ2=3, b=1 Distribution concentrates too much! Be distributed widely
  15. 15. Sequential Filtering 15 xt = xt (A) , xt (B) , xt (C3) ( ), xt A( ) + xt B( ) + xt C3( ) =1State vector: Observation vector: yt = yt A( ) , yt B( ) , yt C3( ) ( ), yt A( ) + yt B( ) + yt C3( ) =1 Prediction p xt y1:t−1( )= p xt xt−1( )∫ p xt−1 y1:t−1( )dxt Filtering p xt y1:t( )∝ p yt xt−1( ) p xt y1:t−1( ) State transition Likelihood Implemented with a Particle Filtering
  16. 16. Experimental results ~data set~ 16 Learning •  907 NBI images (Type A: 359, Type B: 461, Type C3: 87) •  Ensure that the lighting conditions, zooming and optical magnification were kept as similar as possible across different images. •  Images were trimmed by medical doctors and endoscopists. Test video •  4 NBI videoendoscopy sequences (Type A: 2, Type B: 2) •  The length 200 frames, in which polyps were captured largely enough in each image.
  17. 17. Experimental results 17Type BType A Type C3 Original result θ1 = 100, θ2 = 1 θ1 = 100, θ2 = 5 θ1 = 500, θ2 = 1 θ1 = 500, θ2 = 5 Type A_2 (original) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type A_2 (100,1) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type A_2 (100,5) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type A_2 (500,1) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type A_2 (500,5) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type A MRF labeling Type A_2 (original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type A_2 (DP_0.99) frame number 0 20 40 60 80 100 120 140 160 180 200 Type A_2 (Gibbs_p4=0.9)
  18. 18. Experimental results 18Type BType A Type C3 Type B_1 (Original) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (MRF) frame number 0 20 40 60 80 100 120 140 160 180 200 Type B_1 (original) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type B_1 (100,1) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type B_1 (100,5) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type B_1 (500,1) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type B_1 (500,5) 0 20 40 60 80 100 120 140 160 180 200 0.01.0 Type B Original result θ1 = 100, θ2 = 1 θ1 = 100, θ2 = 5 θ1 = 500, θ2 = 1 θ1 = 500, θ2 = 5 MRF labeling
  19. 19. Conclusions •  We have proposed a Particle filter-based smoothing of posterior probability. !  to visualize the output of NBI videoendoscopy recognition. 19 Future work •  Reduce the effects of optical and motion blurs to make recognition more stable. •  Implement the filtering considering label changes. •  Parameter selection and learning. •  Quantitative evaluation.

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